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Data-driven Semi-supervised Machine Learning with Surrogate Safety Measures for Abnormal Driving Behavior Detection.

, , , , and . CoRR, (2023)

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Using Extreme Value Theory for the Prediction of Head-On Collisions During Passing Maneuvres., and . ITSC, page 268-273. IEEE, (2015)Modeling Automated Driving in Microscopic Traffic Simulations for Traffic Performance Evaluations: Aspects to Consider and State of the Practice., , , , , , and . IEEE Trans. Intell. Transp. Syst., 24 (6): 6558-6574 (June 2023)Safety Performance Boundary Identification of Highly Automated Vehicles: A Surrogate Model-Based Gradient Descent Searching Approach., , , , and . IEEE Trans. Intell. Transp. Syst., 23 (12): 23809-23820 (2022)Intelligent Anomaly Detection for Lane Rendering Using Transformer with Self-Supervised Pre-Training and Customized Fine-Tuning., , , , , and . CoRR, (2023)Social-Aware Planning and Control for Automated Vehicles Based on Driving Risk Field and Model Predictive Contouring Control: Driving Through Roundabouts as a Case Study., , , and . SMC, page 3297-3304. IEEE, (2023)Modeling Gap Acceptance in Overtaking: A Cognitive Process Approach., , and . ITSC, page 5925-5931. IEEE, (2023)Safe, Efficient, Comfort, and Energy-Saving Automated Driving Through Roundabout Based on Deep Reinforcement Learning., , , , , and . ITSC, page 6074-6079. IEEE, (2023)Guest Editorial Introduction to the Special Issue on Deployment of Connected and Automated Vehicles in Mixed Traffic Environment and the Implications on Traffic Safety and Efficiency., , and . IEEE Trans. Intell. Transp. Syst., 24 (6): 6432-6435 (June 2023)An Empirical Analysis to Assess the Operational Design Domain of Lane Keeping System Equipped Vehicles Combining Objective and Subjective Risk Measures., , , , , , and . IEEE Trans. Intell. Transp. Syst., 22 (5): 2589-2598 (2021)A Hybrid Spatial-temporal Deep Learning Architecture for Lane Detection., , , and . CoRR, (2021)